Impacts on post-COVID-19 sequelae: A systemic Review
Bibliographic record
Abstract
Concerns on whether or not coronavirus disease 2019 (COVID-19) can cause long-term impact are rising since many aspects of the disease are still under investigation. This systemic review was carried out to recognize the post-COVID-19 sequelae infection in various body systems and to analyze the comorbidities in survivors who suffered long-term impacts. The choice of words used to search is "post-COVID-19," "COVID-19," and "SARS-CoV-2 infection." A total of 1,282 articles were extracted. Reports suggested that a wide range of sequelae were faced by the survivors which affected various systems, mainly the respiratory, gastrointestinal, nervous, cardiovascular, and musculoskeletal systems, while rarely affect other systems such as the endocrine, vascular, renal, and urogenital systems. Comorbidities are involved in determining the harshness of sequelae and the persistence of symptoms of post-severe acute respiratory syndrome coronavirus-2 infection. COVID-19 survivors may present with different symptoms and conditions that vary from mild symptoms to severe and rare conditions. It is crucial to understand the sequelae of post-COVID-19 for prevention and help to establish pandemic control strategies and rehabilitation needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".